What Natural Language Processing Can Do For Your Business?

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What Natural Language Processing Can Do For Your Business?

“Google, call Mom” – how much are you into a habit of asking your phone or another gadget to do something for you, in plain language? If you answered every time or very often, you’d totally understand the importance of Natural Language Processing technology in our lives.  

The rise in demand for better, advanced means to perform is one of the primary causes for technology to evolve at such a pace. So much so that computers can now understand what humans speak in their native language! Of course, this sort of technology wasn’t achieved overnight.

The demand for human-to-machine communication got programmers, coders, and a whole lot of tech specialists to bring out their best.

As humans, we may be able to speak and write English or any other plain language, but for a computer these languages are alien. The machine language or code it understands is largely incomprehensible to most people.

NLP or Natural Language Processing is a branch of artificial intelligence that deals with the interaction between computers and humans using natural language.

The goal of NLP is to read, decipher, analyze, and make sense of the human language in a valuable manner.

Almost any industry one can think of has implemented NLP in its operations, so much so that the common people are used to taking its help in their daily lives. The effect of NLP can be easily noted with the rise in demand for NLP consulting firms or those organizations that provide end-to-end NLP services.

Why is Natural Language Processing important?

Everything we express, through any medium of communication be it verbal or written, carries an enormous amount of information. The way we talk, tone of the conversation, selection of words, or anything that compiles our speech, adds a type of information that can be interpreted and its value, extracted.

NLP helps computers communicate with humans in their native language. It also makes it possible for computers to read a text, hear speech and interpret while determining which parts of the speech are important. Moreover, as machines, they have the ability to analyze more language-based data than humans in a consistent manner, without getting fatigued, and in an unbiased way.

Considering the staggering amount of data that are produced every day be it in the medical industry or social media, automation of language processing will always be critical to analyze speech and data efficiently.

Which techniques are implemented for Natural Language Processing?

Haven’t all of us come across that moment when Alexa or Google replies about not being able to understand what we communicated? Sometimes the computer or device may fail to understand well leading to obscure results. In order to minimize the frequency of such results, there are two main techniques used to accomplish NLP tasks.

  1. Syntax

This refers to how words are arranged in a sentence to make the best grammatical sense. The Process of NLP uses syntactic analysis to assess how the natural language assigns with grammatical rules. A few syntactic techniques that are used are…

– Morphological segmentation: Divides words into individual units called morphemes.

– Lemmatization: Works at reducing a word to its original form and grouping all the different forms of the word, together. 

– Word segmentation: This involves dividing a large piece of continuous text into equal, and distinct units.

– POS tagging: Identifies the part of speech for every word.

– Sentence breaks: Places sentence boundaries on a large piece of text.

– Stemming: Involves striking off an inflected word to its root form.

– Coreference resolution: The task of finding all expressions that refer to the same entity in a text. Coreference resolution is a very important aspect of NLP when it comes to natural language understanding tasks such as document summarization, question answering, and information extraction.

– Stopwords removal: Stopwords are the most commonly used words in any language. When analyzing text data and building NLP models, these stopwords do not add much value to the meaning of the document, like, ‘a’, ‘the’, ‘is’, ‘on’ etc. NLP helps in stopwords removal for a text classification task so that more focus can be given to other words.  

  1. Semantics

Semantics basically involves the meaning that is conveyed by a text. It is one of those problematic aspects of NLP that hasn’t been resolved yet. It requires computer algorithms to understand the meaning and interpretation of words while structuring the sentences. 

Here’s what helps in a semantic analysis…

– NER: Named entity recognition is where parts of a text are determined, identified, and classified as pre-set groups. Examples of such groups are names of people, events, locations, and so on.  

– Word sense disambiguation: Involves giving meaning to a word based on context.

– Natural language generation: uses databases to derive semantic intentions and translate them into human or native language. 

Firms are using NLP for business benefits in multiple ways, some of which are…

  • Getting an objectified and accurate analysis.
  • Streamlining daily operations and processes.
  • Market research and brand positioning.
  • Better decision making through gathered information, rather than assumptions.
  • Reducing costs and effort through automated customer service bots.

How is Natural Language Processing used in different industries?

Typically, Natural Language Processing works in a particular way. A human talks to the machine through the voice input, the machine captures the audio input, audio to text conversion happens, the text data is processed by the AI, data to audio is converted and the machine responds to the user by playing the audio file.

While NLP is considered one of the most difficult things in computer science and engineering, it’s not the work, but the nature of human language that makes it difficult.

NLP makes use of algorithms to identify and extract the natural language rules in such a way that the unstructured language data is translated into a form for the computers to understand. And while this technology has been around for some time, it’s a fascinating extension of AI and has enormously changed how we live in this age of digital transformation.

Here are some of the areas which have been widely using natural language processing in their operations…

  1. Healthcare

A significant challenge for healthcare systems is to utilize their data to its full potential. Electronic medical records contain a huge volume of unstructured data in plain text format. It is a more difficult task to perform analytics on this type of data than on structured data which is common in other industries.

With way too much crucial data to handle manually on a daily basis, Healthcare systems have been moving their records towards a system of Electronic Medical Records. This has resulted in the creation of analytics-driven opportunities to enhance experiences for customers.

NLP has brought about a major change in reducing the manual effort healthcare workers need to put in day in and day out. It helps departments across various healthcare firms to gain insights into health records and other text data.

  1. Psychology and suicide prevention

An estimation from the World Health Organization mentioned that suicide is one of the top few causes of unnatural death worldwide. And every suicide likely affects another 138 people. Now we all are aware that our contact with a health professional is not so frequent enough for them to be able to spot symptoms of being suicidal.

In addition to that, the standard methods of risk assessment followed globally require the individual to disclose their risk of self-harm to the suicide prevention professional.

To make the person concerned comfortable with sharing details about how he or she would be feeling, NLP is implemented. It helps in analyzing and understanding a person’s risk to develop suicidal tendencies. Only recently, Facebook introduced a suicide prevention AI which scans posts on the platform to assess risk. 

  1. Customer service

It is hard to find a firm, irrespective of the industry who do not have a customer service team. Companies who deal with products that are required in daily operations like account details or balance inquiries or even loan inquiries often deploy NLP on the bank’s phone systems, enabling the efficient supply of queries to the right department, minimizing the need for human intervention.

Firms in industries wherein human conversations are necessary, have also been adopting NLP for analytics and using structured data. This branch of AI is also often used to answer business questions to churn new customers through direct query answering.

While bigger firms and reputed brands have NLP services as an in-house necessity, those who do not need it on a constant basis, seek a natural language processing consulting firm.

  1. Investment decisions and online transactions

Investment firms are now beginning to implement NLP to analyze the annual reports and news articles related to their areas of interest. A recent survey by the Economist concluded that AI and advanced analytics could be some of the best possible ways to combat money laundering.

Considering the rate at which globalization and digitalization are competing with each other in terms of growth, the volume of online transactions for investment has seen an exponential rise.

The conventional rule-based transaction systems are limited in their potential when it comes to detecting money laundering activity. Banks are therefore advising to implement supporting analytics-driven capabilities in detecting money laundering activity.  

  1. Pharmaceutical

There are multiple stages in producing a drug. From drug discovery to human trials and what-not, a drug development process involves a multitude of information that is safety-related which is buried as unstructured data.

In an industry that deals with patient safety day in and day out data like internal safety reports, medical literature, electronic medical records, conference proceedings, etc can be extracted as input for safety information. NLP models are incorporated into analytics and decision-making processes to allow researchers a peek into the best action possible.  

Take, for example, the German Pharma company Boehringer Ingelheim. They are said to have a model wherein a clinical trial protocol is analyzed and various measures of the trial complexity are fed into a cost model. This allows them to analyze and run a trial without having to spend a lot of time reading the protocols that need to be followed. 

  1. Finance and Legal

When you purchase a product how often do you happen to actually read the terms and conditions before clicking on, I Agree? Natural Language Processing solutions are being used to extract key information from unstructured and lengthy documents and classifying them according to the requirement of the firm.

Since no two legal documents are the same, it is difficult to divide the documents into respective categories using non-AI programming techniques.

Moreover, as the need to anonymize data for compliance has sprung with increased regulation, several products have appeared in the market particularly since the advent of gross domestic product regulation.

Use Cases

Natural Language Processing in fighting against a pandemic:

When there was an outbreak of the Coronavirus in China, there was way too much information floating around in documents of those affected and the healthcare facilities.

An NLP model was developed that supported data screening and analysis of texts in medical records. This epidemiological investigation helped in the fight against Covid 19 in China cities.

Natural Language Processing in security authentication:

The question generation feature of NLP is being widely used by data scientists to build stronger security systems.

This is achieved by finding an additional context for the user’s information, and extracting the relevant answer using a named entity recognition model. The user’s answer gets validated only after it is found in sync with the generated question for the neural network.

Natural Language Processing in Market Research and Branding:

It is not easy to come up with marketing strategies when you are unclear about how customers feel about your product. By implementing sentiment analysis from NLP, you can make out when you receive positive and negative feedback.

An NLP-based software analyses social media content including reviews and ratings and converts them into insightful data. This helps in strategizing about your brand’s strengths and weaknesses based on the information provided.

Natural Language Processing in Edtech:

When it’s about saving time for both the learners and administrative staff in the educational sector, NLP has its major role in achieving that.

It generally identifies and analyses the strengths or weaknesses of students with respect to the requirements developed through a personalized curriculum diagnosis. Talking about an Edtech unicorn, SquirrelAI, it helps to learn about students in hours which would otherwise take years by finest tutors as quoted by the founder Derek Li.

Another boon for the staff in the educational sector is PrepAI, an AI-powered software that helps the teachers to generate the best question sets out of any specific topic. It also has an in-built grading system for students to improvise in certain areas.

Natural Language Processing in Competitive Analysis:

While there are multiple tools for founders and top executives to conduct competitive analysis and research while starting a business, NLP-powered platforms like Zirra simplifies the process for automatically generating a competitive environment for you.

During analysis, Zirra gathers a list of companies and ranks them from zero to one. This ranking is based on how closely the firms are related to each other in terms of objectives, and NLP uses a multimodal semantic field to do so.


Currently, Natural Language Processing is battling difficulties in language meaning, due to lack of context, spelling errors, or dialectal differences. However, the point that also cannot be ignored is that NLP plays a key role in supporting human-to-machine interactions.

As research continues in this field, there are more breakthroughs expected to make machines smarter at recognizing and understanding the human language.

Private organizations, MNCs, and the public sectors have been applying AI and ML technologies to their use. And one of the quickest evolving AI technologies today is NLP.  So, having the right partner to cater to your AI, ML, or NLP requirements is very important in this age.

The future of NLP looks challenging for sure, but what cannot be overlooked is the fact that it has been developed and implemented by businesses at a much faster pace against what was expected.

In the coming years, we’d likely reach a stage where this technology will have advanced to a level that would make complex applications in industries possible and easier.

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